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Author SHA1 Message Date
Vivek Khandelwal d35b6b412a
[ONNX] Add OnnxToTorch Lowering for Sequence Ops (#3425) 
This commit adds the lowering for SequenceAt, SequenceEmpty,
SequenceInsert, SequenceErase op

Signed-Off By: Vivek Khandelwal<vivekkhandelwal1424@gmail.com>
 2024-06-08 09:58:11 +05:30
aldesilv f794582b18
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421) 2024-06-07 14:04:11 -05:00
Vivek Khandelwal 1a9c0a35a9
[Onnx] Add Onnx->Torch lowering for Onnx.Shrink Op (#3385) 
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-07 22:47:27 +05:30
Suraj Sudhir 1c2778dd56
[ONNX] Conv op adds support for asymmetric padding. (#3426) 
Supports asymmetric padding by performing a torch.nn.functional.pad on
the input before performing the convolution.

Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
 2024-06-07 09:54:39 -07:00
Sambhav Jain d0a818a03e
Representing Symbolic Shape Expressions in Torch Dialect (#3372) 
Torch Dialect with symbolic shape expressions:
```ll
module {                                                                                                                                                                                                     
  func.func @main(%arg0: !torch.vtensor<[?,?,3],f32>, %arg1: !torch.vtensor<[?,?,3],f32>) -> !torch.vtensor<[?,?,3],f32> {                                                                                   
    %0 = torch.symbolic_int "s0" {min_val = 5, max_val = 10} : !torch.int                                                                                                                                    
    %1 = torch.symbolic_int "s1" {min_val = 0, max_val = 100} : !torch.int                                                                                                                                   
    %2 = torch.symbolic_int "s3" {min_val = 0, max_val = 50} : !torch.int                                                                                                                                    
    
    torch.bind_symbolic_shape %arg0, [%0, %1], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>                                                                                          
    torch.bind_symbolic_shape %arg1, [%0, %2], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>                                                                                          
    
    %3 = torch.aten.tanh %arg0 : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32>                                                                                                                  
    torch.bind_symbolic_shape %3, [%0, %1], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>                                                                                             
    
    %4 = torch.aten.sigmoid %arg1 : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32>                                                                                                               
    torch.bind_symbolic_shape %4, [%0, %2], #affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>                                                                                             
    
    %5 = torch.prim.ListConstruct %3, %3, %4 : (!torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>) -> !torch.list<vtensor>                                               
    %int1 = torch.constant.int 1                                                                                                                                                                             
    %6 = torch.aten.cat %5, %int1 : !torch.list<vtensor>, !torch.int -> !torch.vtensor<[?,?,3],f32>                                                                                                          
    torch.bind_symbolic_shape %6, [%0, %1, %2], #affine_map<()[s0, s1, s2] -> (s0, s1 * 2 + s2, 3)> : !torch.vtensor<[?,?,3],f32>                                                                            
    
    return %6 : !torch.vtensor<[?,?,3],f32>                                                                                                                                                                  
  }                                                                                                                                                                                                          
}              
```

For reference, this is the TorchDynamo exported program with symbolic
shape expressions that the above Torch dialect program is imported from:
```py
ExportedProgram:                                                                                                                                                                                             
    class GraphModule(torch.nn.Module):                                                                                                                                                                      
        def forward(self, x: "f32[s0, s1, 3]", y: "f32[s0, s3, 3]"):                                                                                                                                         
            # File: /home/sambhav.jain/workspaces/cruise/src/3p/torch-mlir/test/python/fx_importer/symbolic_shape_expr_test.py:31 in forward, code: a = torch.tanh(x)                                        
            tanh: "f32[s0, s1, 3]" = torch.ops.aten.tanh.default(x);  x = None                                                                                                                               
                                                                                                                                                                                                             
            # File: /home/sambhav.jain/workspaces/cruise/src/3p/torch-mlir/test/python/fx_importer/symbolic_shape_expr_test.py:32 in forward, code: b = torch.sigmoid(y)                                     
            sigmoid: "f32[s0, s3, 3]" = torch.ops.aten.sigmoid.default(y);  y = None                                                                                                                         
                                                                                                                                                                                                             
            # File: /home/sambhav.jain/workspaces/cruise/src/3p/torch-mlir/test/python/fx_importer/symbolic_shape_expr_test.py:33 in forward, code: return torch.cat((a, a, b), dim=1)                       
            cat: "f32[s0, 2*s1 + s3, 3]" = torch.ops.aten.cat.default([tanh, tanh, sigmoid], 1);  tanh = sigmoid = None                                                                                      
            return (cat,)                                                                                                                                                                                    
                                                                                                                                                                                                             
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None, persistent=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='y'), target=None, persistent=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='cat'), target=None)])                                               
Range constraints: {s0: ValueRanges(lower=5, upper=10, is_bool=False), s1: ValueRanges(lower=0, upper=100, is_bool=False), s3: ValueRanges(lower=0, upper=50, is_bool=False)} 
```

Huge credit to @stellaraccident for the inputs that helped evaluate the
various design options and arrive at the representation of choice.


- [x] Op definitions for symbolic_int and bind_symbolic_shape ops
- [x] fx_importer updates to import range constraints + create
symbolic_int ops
- [x] fx_importer changes for AffineMapAttr building + adding
bind_symbolic_shape ops
- [x] custom printer/parser for inlined AffineMap expressions in mlir
assembly
- [x] Dialect lit test
- [x] fx_importer python lit tests
- [ ] Cleanup pass to remove these ops (can add in a follow-on)
 2024-06-07 04:04:03 -07:00
Vivek Khandelwal 72837fbb3d
build: manually update PyTorch version (#3340) 
Set PyTorch and TorchVision version to nightly release 2024-05-14.

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-06 22:23:40 +05:30
Vivek Khandelwal 35dd8c52cd
[ONNX] Add OnnxToTorch Lowering for MaxUnpool op (#3413) 
This commit also adds the Torch declaration for aten.max_unpool2d and
aten.max_unpool3d op. The TorchToLinalg lowering for the same will be
added in a follow-up commit.

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-04 21:09:53 +05:30
Xida Ren (Cedar) 11c3281a8a
Fix reducesum onnx lit test to linalg lowering fails (#3218) 
fixes https://github.com/nod-ai/SHARK-Turbine/issues/653

---------

Co-authored-by: Xida Ren <xida.ren.dev@gmail.com>
 2024-06-03 16:36:09 -04:00
Vivek Khandelwal 6382dbbcc0
[ONNX] Add OnnxToTorch lowering for SpaceToDepth op (#3393) 
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-06-03 20:29:39 +05:30
Surya Jasper fc100a117d
[MLIR][ONNX] Add OnnxToTorch support for Scatter Op (#3400) 
This PR adds OnnxToTorch support for Scatter op
 2024-05-31 07:36:48 +00:00
zjgarvey 074098d20c
Modifies onnx resize lowering to fix numerical issues (#3381) 
Updates:

- some unsupported modes are now going to report a match failure for
unsupported coordinate transformation modes.
- fixes a bug that was introduced in the last patch for resize (my
bad...)
- uses actual x and y coordinates for computing weights in bilinear
interpolation (rather than eps modified values)
- slightly simplifies the bilinear interpolation payload for readability
and performance
- passes coordinate transformation mode information from an onnx.Resize
op to the mode string for the aten._interpolate op. This allows us to
perform custom logic in the torch->linalg lowering to support
onnx.Resize options without losing the default behaviors of the
interpolate op.
 2024-05-30 20:34:37 -04:00
Vivek Khandelwal d7b8f00d01
[ONNX] Add OnnxToTorch Lowering for LpNormalization op (#3397) 
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
 2024-05-30 23:05:26 +05:30
Angel Zhang 2e194e13d6
[Torch] Fix bugs for `Torch::AtenOneHotOp` (#3350) 
This PR fixes the bugs for `Torch::AtenOneHotOp` by:

1) Using `Torch::kUnknownSize` as the default value for `numClasses` in
   the pattern matching stage in `DecomposeAtenOneHotOp`
2) Adding `AtenIntScalarOp` to the patterns in `TorchToArith`
3) Handling both `int` and `float` types for `off` and `on` values in
`TorchOnnxToTorch` conversion

It also includes:

1) A new test in `TorchToArith/basic.mlir`, for `torch.aten.Int.Scalar`,
and
2) A new test in `decompose-complex-ops.mlir`, for `torch.aten.one_hot`

**Dependencies**

This PR is dependent on #3334.
 2024-05-22 17:19:08 +00:00
Yuanqiang Liu f4bfe3f948
Bump llvm and stablehlo (#3377) 
* bump llvm to 1e5f29af81a5f6fda308074f6345b9fba4faa71c
* bump stablehlo to c44d9af8d4879adccf1054cb61a53377ae5898cb
 2024-05-22 23:28:45 +08:00
Angel Zhang 52be4bdc18
[ONNX] Fix bugs for the `onnx.OneHot` operator (#3334) 
This commit fixes the bugs for the `onnx.OneHot` operator by:

1) Converting negative indices to non-negative indices
2) Handling both `int` and `float` types for `off` and `on` values
3) Using the correct result type

It also includes a new unit test.
 2024-05-22 08:32:00 -04:00
Aart Bik 560ca24771
[torch-mlir][sparse] replace xavier with ones initialization (#3374) 
ensures stability of results between different set ups
 2024-05-21 17:12:55 -07:00
RattataKing fcf48872b3
[ONNX] Implement Softsign op (#3373) 2024-05-21 12:10:26 -07:00
Aart Bik c0e7d2667d
[torch-mlir][sparse] inference mode for sparse GCN test (#3369) 2024-05-20 19:52:16 -07:00
zjgarvey 297c270980
onnx.Resize and aten._interpolate : allow n spatial dims. (#3368) 
The old lowering only had logic for 2d (i.e. images). this patch allows
interpolation for n spatial dims, which is required for some 3d vision
models such as

- onnx/models/pytorch-3dunet_vaiq_int8

which successfully compiles and runs with this patch.
 2024-05-20 13:35:27 -07:00
lialan 99511cef82
Implement `onnx.Hardmax` lowering (#3342) 
Co-authored-by: Ubuntu <xunli@wsno1.judsoscro3wupi0qm4bjlj5m3b.bx.internal.cloudapp.net>
Co-authored-by: Hasekawa-Takumi <bewater.private476@passmail.net>
 2024-05-20 20:56:24 +05:30
Wu Yuan cc28d566ff
[Stablehlo] Support AtenTrilOp (#3359) 
1. lower aten.tril to stablehlo composed by iota, select and so forth
2. add related e2e test cases
 2024-05-20 15:49:24 +08:00
Aart Bik e80f072ba4
[torch-mlir][sparse] example of a sparse graph convolution (#3363) 2024-05-17 15:43:50 -07:00
zjgarvey 6cba93b16e
[ONNX][TorchToLinalg] Add support for dynamic dims in Interpolate lowering (#3351) 
Addresses [Shark-Turbine
#196](https://github.com/nod-ai/SHARK-TestSuite/issues/196)

Related tracker [Shark-Turbine
#566](https://github.com/nod-ai/SHARK-Turbine/issues/566)

Related onnx.Resize issues [Shark-Turbine
#616](https://github.com/nod-ai/SHARK-Turbine/issues/616)
 2024-05-17 12:18:57 -07:00
Andrew Woloszyn 513d89c16d
Add support for the onnx.SequenceLength op. (#3362) 2024-05-17 12:17:43 -07:00
Andrew Woloszyn 72e38dcbbc
Add support for the onnx.SequenceConstruct op. (#3316) 2024-05-17 22:51:28 +05:30
Peiming Liu ccb772cd0f
[sparse] propagate sparsity properly when decompose torch operations. (#3318) 2024-05-15 10:09:27 -07:00
Aaron St George ba32b9cee7
Don't fold `aten.clone` if result isn't same type as input (#3347) 
Similar to https://github.com/llvm/torch-mlir/pull/2824, we were seeing
some assertion failures after the addition checks around folders were
tightened up in LLVM: https://github.com/llvm/llvm-project/pull/75887 .
This PR essentially moves the logic that used to be applied at the LLVM
level into the folder, which seems to be the suggested fix.
 2024-05-16 00:07:45 +08:00
Aart Bik 44fa6c3afd
[torch-mlir][sparse] sparse diagonal feature scaling test (#3344) 2024-05-14 12:13:54 -07:00
Peiming Liu 8e74d64e8f
[sparse] convert to sparse before any use in sparse test. (#3337) 2024-05-14 09:10:36 -07:00
NeverRaR 26b78285bf
[MLIR][ONNX] Add OnnxToTorch support for GlobalMaxPool Op (#3232) 
https://github.com/nod-ai/SHARK-Turbine/issues/658

---------

Co-authored-by: root <root@i32b01216.sqa.eu95>
 2024-05-14 15:55:39 +05:30
Archana Ramalingam 20f312853c
[MLIR][ONNX] Add OnnxToTorch support for ReduceLogSumExp Op (#3201) 
This commit adds the OnnxToTorch support for ReduceLogSumExp op
 2024-05-14 09:54:26 +05:30
Aart Bik 667dfcbc5a
[torch-mlir][sparse] enable test on ReLu (#3336) 
Downstream MLIR sparsifier has some (rudimentary) support for ReLU now,
and this test can now be enabled with correct end-to-end behavior.

Also see discussion at:

https://discourse.llvm.org/t/min-max-abs-relu-recognition-starter-project/78918
 2024-05-13 15:34:26 -07:00
zjgarvey 75d1d72059
Generalize Operand Quantization in FuseQuantizeOps (#3327) 
This change enables more customization with operand quantization, and
generalizes the patterns QuantizeOperands and QuantizeTransposeOperands
to QuantizeOperandsPastCommutingOps.

This allows for passing quantization through operations which are
functionally unaffected by quantization, such as view-like ops. The
purpose of this change is to address a myriad of quantization issues
seen in quantized onnx models that have some reshape-like operations
sandwiched in between a dequant and something like a matmul (whose other
operand is immediately quantizable).
 2024-05-12 20:49:59 -07:00
Yuanqiang Liu 0b7cbf5e60
[Stablehlo] fix aten.randn's lowering with f32 element type (#3329) 2024-05-11 17:40:04 +08:00
Yuanqiang Liu 5f7cb9e253
[Stablehlo] lowering aten.randn & aten.normal_functional to mhlo.rng … (#3328) 
…NORMAL

* split lowering of uniform, randn, normal from Basic.cpp into Rng.cpp
 2024-05-11 15:33:37 +08:00
Stella Laurenzo 00efec0b73
[linalg] Implement strict mode lowering for aten.view. (#3319) 
* Enables assume_strict_symbolic_shapes on fx_importer imported
programs, indicating strict shape semantics.
* Reworks the view->reshape lowering to take advantage of strict mode
and do one of:
  * Collapse to 0D
  * Flatten/Unflatten when there is an inferred dim.
  * Fallback to tensor.reshape
* Splits some test cases up and adds an attribute to control the old
pattern (so new corners can be tested in strict mode in isolation).
* Dynamic inferred mode needs upstream work to generalize expand_shape
(so that case is suppressed here).
* Deletes the assert from the existing tensor.reshape lowering if strict
shape mode is enabled (since the condition it is dynamically asserting
cannot happen).
 2024-05-10 13:45:50 -07:00
Andreas Falkenberg adafd51823
[onnx] Gridsampler addition of nearest mode (#3320) 
Added nearest neighbor selection for onnx.Gridsampler
 2024-05-10 11:42:10 -07:00
jinchen 4b24909427
Add attributes support for onnx cumsum op (#3241) 2024-05-11 02:09:01 +08:00
NeverRaR 1d4859699b
MaxPool1d lowering to linalg (#3295) 
Co-authored-by: root <root@i32b01216.sqa.eu95>
 2024-05-10 22:05:26 +05:30
Angel Zhang 261074f594
[ONNX] Handle one-input case for Min ONNX operator (#3326) 
This commit handles the one-input case for the "Min" ONNX operator. A
new unit test has also been added.
 2024-05-10 22:04:03 +05:30
Angel Zhang 7c289d9522
[ONNX] Handle one-input case for `onnx.Max` operator (#3325) 
This commit handles the one-input case for the "Max" ONNX operator. A
new unit test has also been added.
 2024-05-10 08:58:46 -07:00
Peiming Liu 2c22087cab
[sparse] match fx node using target name instead of variables name (#3315) 2024-05-09 12:34:14 -07:00
penguin_wwy afe87d62b4
[Linalg] [Stablehlo] Promote type for compare scalar op (#3306) 2024-05-10 02:20:06 +08:00
Aart Bik 97a822de0a
[torch-mlir][sparse] minor tweaks in sparse tests (#3311) 
(1) test full pytorch output for eltwise
(2) use "random" input for LIF, to get general sparse tensor 
(3) introduce way to get true sparsity into network (needs backend fix
first)
 2024-05-09 10:03:25 -07:00
Aart Bik a033bbfe6c
[torch-mlir][sparse] recognize to_dense primitive (#3308) 
also maps simply to sparse_tensor.convert
the sparsity types do the rest!
 2024-05-08 22:50:17 -07:00
Aart Bik 89bb7404c1
[torch-mlir][sparse] add a true network to our NN tests (#3305) 
Objective: make the to_sparse work end-to-end!
 2024-05-08 21:18:42 -07:00
Peiming Liu cff144b3ac
[sparse] fix double free due to incompatibility between buffer-deallo… (#3303) 
…cation and sparse tensors.

**NOTE**: This PR _doges_ the issue in buffer-deallocation pass instead
of resolving it. In the future, we need to fix the bug in
buffer-deallocation pass when handling code generated by sparse
compiler.
 2024-05-08 21:18:17 -07:00
Yuanqiang Liu 5213557b87
[Stablehlo] fix lowering gelu(x, tanh) (#3307) 
* lowering gelu("none") to erf
* lowering gelu("tanh") to tanh
 2024-05-09 11:39:13 +08:00
Aart Bik c4b28e8d9f
[torch-mlir][sparse] test for sparse "activation" (#3304) 
Example of introducing sparsity into the forward pass. With a bespoke
propagation (but upstream PyTorch will support this).
 2024-05-08 19:01:24 -07:00
aldesilv ec6d7aa5d2
OnnxToTorch lowering resize op (#3013) 
https://github.com/nod-ai/SHARK-Turbine/issues/358
adds a lowering from onnx to linalg for bilinear and nearest resize with
support for using scales or sizes to get resize shape. uses coordinate
transform half pixel for bilinear mode and asymmetrical for nearest
mode. See
https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added
two passes -- one for bilinear and the other for nearest.
 2024-05-08 21:35:03 +00:00